PATTERN-RECOGNITION STUDIES OF TANDEM MASS-SPECTRA

Principal components analysis and pattern recognition (PCAPR) techniqu es were applied to MS-MS spectra of fourteen organic compounds. Each s pectrum was represented as a two-dimensional matrix containing informa tion from the MS1 spectrum as well as from one, two or three MS2 spect ra. The data were reduced by calculating a one-principal component mod el for each spectrum which explained between 86 and 99% of the varianc e. Each model was used to calculate each of the spectra, and residual standard deviations (R.S.D.s) were used as a measure of spectral simil arity: low R.S.D.s (< 1.0) corresponding to similar spectra and higher R.S.D.s (> 1.0) to dissimilar spectra. The system shows promise for u se in monitoring situations in that MS-MS spectra can be efficiently r educed and stored as principal components models and R.S.D. calculatio ns can be used to identify a compound based on how well its spectrum i s predicted by the available reference models.